Quality evaluation of Chinese red wine based on cloud model

Area of Science:

• Food Science
• Artificial Intelligence
• Data Analysis

Background:

• Red wine quality determination involves numerous qualitative and quantitative factors.
• Traditional evaluation methods struggle with the inherent complexity and uncertainty of wine quality assessment.
• The global red wine market is substantial, with China being the largest consumer since 2013.

Purpose of the Study:

• To introduce the cloud model, a novel approach for uncertainty transformation, into red wine quality evaluation.
• To propose a new comprehensive cloud model algorithm based on the addition of two cloud models.
• To validate the efficacy of the cloud model in red wine quality assessment.

Main Methods:

• Development of a novel comprehensive cloud model algorithm.
• Application of the cloud model to red wine quality evaluation.
• Validation using gray relational analysis and fuzzy evaluation methods.

Main Results:

• The proposed cloud model successfully evaluated a red wine sample as 'Good'.
• The results demonstrated the model's capability to handle the uncertainty inherent in red wine quality assessment.
• Validation confirmed the suitability of the cloud model for this application.

Conclusions:

• The cloud model offers a robust method for evaluating red wine quality by addressing fuzziness and randomness.
• This novel approach provides a valuable tool for the wine industry, enhancing quality assessment accuracy.
• The study successfully integrated artificial intelligence techniques with sensory evaluation for wine quality analysis.